Alex Blank
34 lines
2.1 KiB
TeX
34 lines
2.1 KiB
TeX
%! Author = alex
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%! Date = 3/6/25
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\section{Conclusion}\label{sec:conclusion}
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This thesis presented a systematic investigation of different machine learning architectures
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for fertility prediction based on high-resolution body core temperature data.
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By comparing LSTM- and Transformer-based models, as well as their convolutional variants,
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the results show that machine learning can achieve high predictive performance.
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LSTM models performed best according to standard evaluation metrics, whereas Transformer-based models
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proved more robust in simulated use case evaluations for contraception and Natural Family Planning (NFP).
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Predictions were consistently more reliable in regular cycles than in irregular ones,
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highlighting both the potential and the inherent limits of temperature-based approaches.
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A characteristic pre-ovulatory temperature drop was identified as correlating with fertility.
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Both its timing and its magnitude appear to influence fertility probability,
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pointing to a concrete physiological marker that could be exploited in practice.
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Use case evaluations indicate that the model outputs could be relevant for contraception and NFP\@.
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In simulations, model-guided decisions reduced unintended pregnancies relative to naive and
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calendar baselines and improved the efficiency (pregnancies per 1000 intercourse events) of timed intercourse.
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These results are simulation-based and depend on assumptions about intercourse
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patterns and fecundability; they should not be interpreted as clinical effectiveness estimates or as
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direct comparisons to established contraceptive methods.
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Despite the limitations of real-world tracking data, including missing entries, noise,
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and user heterogeneity, this work underscores the potential of personalized, data-driven predictions
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in digital reproductive health.
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Future work should integrate additional physiological signals, expand demographic representation,
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and validate models in prospective real-world settings.
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In sum, this study contributes a systematic foundation for machine learning-based fertility prediction
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and sets the stage for adaptive, user-tailored fertility support tools. |